Ranjan SINGH

Research topics:

Development of terahertz, infrared, and optical metamaterial based active and passive plasmonic devices.

ASTAR_M4070314_BRENIAC

NRF-CRP23-2019-0005

QUANTUMET_ANR_NRF

T2EP50121-0017 (MOE Tier 2)

THz metamaterials

Tier 1 RG191/17

Tier 1 RG96/19

Tier-2, MOE2017-T2-1-110

TRIM_M22L1b0110

Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

521 to 530 of 1,220 Results

theta = 25, phi = 50.tab Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications Tabular Data - 1000.3 KB - 340 Variables, 339 Observations - UNF:6:S8colQsykhGUDAgWGPlQxw== Required data for Figure 9.
theta = 25, phi = 95.csv Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications Comma Separated Values - 1003.8 KB - MD5: a22c3ebb53f165681a2ce4e468c746f2 Required data for Figure 9.
theta = 30, phi = 100.tab Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications Tabular Data - 1000.6 KB - 340 Variables, 339 Observations - UNF:6:zAHyTQahbK0ZjrT0z9FVaA== Required data for Figure 9.
transmission [f = 1 THz, p = 75 um, lz = 200 um].txt Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications Plain Text - 69.1 KB - MD5: faa231c9fce53e679dfe828df02c76d4 Required data for Figure 8.
twilight.m Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications MATLAB Source Code - 715 B - MD5: f0482d5c5e70b619d1cc2a6c0fb903c4 Matlab script: User-defined colormap
xN.txt Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications Plain Text - 335 B - MD5: 1205cd83a47bff913e7a988d0561055a Required data for Figure 9 and 10.
yM.txt Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications Plain Text - 335 B - MD5: 1205cd83a47bff913e7a988d0561055a Required data for Figure 9 and 10.
10um.csv Jul 8, 2022 - Replication Data for: Topological sensor on a silicon chip Comma Separated Values - 70.7 KB - MD5: 1e49331e19ba6255b0e14f63eecb2b67
10um_PTI.txt Jul 8, 2022 - Replication Data for: Topological sensor on a silicon chip Plain Text - 70.3 KB - MD5: 7ef3d9387376e60b464d957a5c174a54
12.5_1um.csv Jul 8, 2022 - Replication Data for: Topological sensor on a silicon chip Comma Separated Values - 70.7 KB - MD5: 2e65caa242f35ad12edbb802d979a7f1

theta = 25, phi = 50.tab

Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications

Tabular Data - 1000.3 KB - 340 Variables, 339 Observations -

Required data for Figure 9.

theta = 25, phi = 95.csv

Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications

Comma Separated Values - 1003.8 KB -

Required data for Figure 9.

theta = 30, phi = 100.tab

Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications

Tabular Data - 1000.6 KB - 340 Variables, 339 Observations -

Required data for Figure 9.